Surdeanu and Manning (2010) also show that the diversity of parsers is important for performance improvement when integrating different parsers in the supervised track.

we can conclude that co-training helps dependency parsing, especially when using a more divergent parser.


To do this, we apply our definitional similarity measure introduced in Section 2.1.

having found the intended senseĉ w i of w i , we add the edge {c,ĉ w i } to E. of this procedure, we obtain a semantic graph representation G for the lexicon L. As an example, consider the 4 th sense of the noun cone in Wiktionary (i.e., cone 4 n ) which is defined as "The fruit of a conifer".


To ease the use of lexical neighbours in our experiments, we merged together predicates that include the same lexical unit, a posteriori.

there is no need for a syntactic analysis of the context considered when exploiting the resource, and sparsity is less of an issue 1 .


We trained JNNSE(fMRI+Text) with 30 words, and tested word prediction with the remaining 8 subjects and the other 30 words.

the training and testing word sets are disjoint.


Our findings also indicate that using the brain as a semantic test can separate models that capture this additional semantic information from those that do not.

the brain is an important source of both training and testing data.


Imagine a situation where a negotiator is so uncooperative and arrogant that the other negotiators decide to completely change their negotiation strategy in order to punish her.

it is important to investigate RL approaches that do not make such assumptions about the user/environment.


Space constraints prevent us from providing an exhaustive list of previous work on using RL for dialogue management.

below we focus only on research that is directly related to our work, specifically research on concurrent learning of the policies of multiple agents, and the application of RL to negotiation domains.


Here two or more agents learn simultaneously.

the environment of an agent is one or more other agents that continuously change their behavior because they are also learning at the same time.


Also, they have human-like constraints of imperfect information about each other; they do not know each other's reward function or degree of rationality (during learning our agents can be irrational).

a Nash equilibrium (if there exists one) cannot be computed in advance.


Given that Agent 1 is more interested in apples and Agent 2 cares more about oranges, the maximum total utility solution would be the case where each agent offers to get all the fruits it cares about and to give its interlocutor all the fruits it does not care about, and the other agent accepts this offer.

when converging to the maximum total utility solution, in the case of 4 fruits (4 ap-ples and 4 oranges), the average reward of the two agents should be 1200 minus 10 for making or accepting an offer.


For example, in the case of 4 fruits, if Agent 1 starts the negotiation, after converging to the maximum total utility solution the optimal interaction should be: Agent 1 makes an offer to Agent 2, namely 0 apples and 4 oranges, and Agent 2 accepts.

the reward for Agent 1 is 1190, the reward for Agent 2 is 1190, and the average reward of the two agents is also 1190.


Also, n r is the number of runs (in our case always equal to 20).

in the case of 4 fruits, we will have CR 1 =CR 2 =1200, and if for all runs R 1j =R 2j =1190, then AD=10.


For 7 fruits PHC-W appears to perform worse than Q-learning but this is because, as we can see in Figure 5, in this case more than 400,000 episodes per epoch are required for convergence.

after only 100,000 episodes per epoch all policies still behave somewhat randomly.


As shown by Feng and Hirst (2012), for a pair of discourse constituents of interest, the sequential information from contextual constituents is crucial for determining structures.

it is well motivated to use Conditional Random Fields (CRFs) (Lafferty et al., 2001), which is a discriminative probabilistic graphical model, to make predictions for a sequence of constituents surrounding the pair of interest.


Because the structure model is the first component in our pipeline of local models, its accuracy is crucial.

to improve its accuracy, we enforce additional commonsense constraints in its Viterbi decoding.


's computation of intra-sentential sequences depends on the particular pair of constituents: the sequence is composed of the pair in question, with other EDUs in the sentence, even if those EDUs have already been merged.

different CRF chains have to be formed for different pairs of constituents.


Since those non-leaf constituents are already labeled in previous steps in the tree-building, we can now re-assign their relations if the model predicts differently in this step.

this re-labeling procedure can compensate for the loss of accuracy caused by our greedy bottom-up strategy to some extent.


It is fairly safe to assume that each m k is a constant, in the sense that m k is independent of the total number of sentences in the document.

the total time complexity Σ n k=1 O(m 2 k ) ≤ n × O(max 1≤ j≤n (m 2 j )) = n × O(1) = O(n), i.e., linear in the total number of sentences.


Adopting a greedy approach, on an arbitrary level during the tree-building, once we decide to merge a certain pair of constituents, say U j and U j+1 , we only need to recompute a small number of chains, i.e., the chains which originally include U j or U j+1 , and inference on each chain takes O(1).

the total time complexity is (n − 1) × O(1) + (n − 1) × O(1) = O(n), where the first term in the summation is the complexity of computing all chains on the bottom level, and the second term is the complexity of computing the constant number of chains on higher levels.


First, as shown in Table 2, the average number of sentences in a document is 26.11, which is already too large for optimal parsing models, e.g., the CKY-like parsing algorithm in jCRF, let alone the fact that the largest document contains several hundred of EDUs and sentences.

it should be seen that non-optimal models are required in most cases.


It shows that the best performance is achieved when T = 50 with 51.11 in F1).

we set T as 50 in our following experiments.


In practice, the use of likelihood ratios tends to result in significant improvements in text-analysis performance.

in our problem, LRT computes a contingency table of a pattern p and a word w, derived from the corpus statistics, as given in Table 3, where k 1 (w, p) is the number of documents that w matches pattern p, k 2 (w,p) is the number of documents that w occurs while p does not, k 3 (w, p) is the number of documents that p occurs while w does not, and k 4 (w,p) is the number of documents containing neither p nor w. Based on the statistics shown in Table 3, the likelihood ratio tests (LRT) model captures the statistical association between a pattern p and a word w by employing the following formula: where: the utility of a pattern can be measured as follows: where W is the current word set used in the algorithm (see Algorithm 1).


In such words, each single character has high probability to be a word.

we design the following measure to favor this observation.


New words are usually multi-word expressions, where a variety of statistical measures have been proposed to detect multi-word expressions.

such measures can be naturally incorporated into our algorithm.


Note that at the early stage of Algorithm 1, larger k p (perhaps with noisy patterns) may lead to lower quality of new words; while larger k w (perhaps with noisy seed words) may lead to lower quality of lexical patterns.

we choose the optimal setting to small numbers, as k p = 5, k w = 10.


Note that ReNew captures and uses the sentiment changes.

our segmentation algorithm considers only two specific types of transi-tion cues including contradiction and emphasis.


Each partial sentence is annotated with its semantic information, built up using the semantic annotations associated with the PLTAG trees.

we use as a reward a measure of the match between the semantic annotation of the partial tree and the communicative goal.


STRUCT formalizes the generation problem as an MDP and applies a version of the UCT algorithm, a fast online MDP planner, to solve it.

sTRUCT naturally handles probabilistic grammars.


A state-of-the-art approach for generating such text is based on an expert annotator whose job is to decide which words to "switch out" with novel foreign words (from hereon we will refer to the "switched out" word as the source word and to the "switched in" word as the target word).

the process is labor-intensive and leads to a "one size fits all solution" that is insensitive to the learner's skill level or vocabulary proficiency.


We perform a one-way ANOVA across the four groups listed above, with the resulting F = 11.38 and p = 9.7e−7.

multiple pairwise comparison of the models was performed with the Bonferroni-corrected pairwise t-test, yielding the only significantly different recall means between HF − M AN (p = 0.00018), RAN DOM − M AN (p = 2.8e − 6), RAN DOM − OP T (p = 0.00587).


partof-speech (POS) tagging or parsing).

the Chinese relation extraction is more difficult.


Both approaches are based on the Chinese characteristics.


However, the mapping of two entities involved in a relation instance may leads to errors.

kim and Lee (2012) further employ a graph-based semisupervised learning method, namely Label Propagation (LP), to indirectly propagate labels from the source language to the target language in an iterative fashion.


However, the Chinese corpus contains 633 documents and 9,147 positive relation instances while the English corpus only contains 498 files and 6,253 positive instances.

in order to balance the corpus scale to fairly evaluate bilingual active learning impact on relation classification, we randomly select 458 Chinese files and thus get 6,268 positive instances, comparable to the English corpus.


In our formulation, a soft-constrained model imposes a penalty for each unsatisfied constraint, proportional to the amount by which it is violated.

our derivation parallels how soft-margin SVMs are derived from hard-margin SVMs by introducing auxiliary slack variables (Cortes and Vapnik, 1995).


Consider the optimization problems of the form: For positive c i , it is clear that an optimal z i will be equal to the degree to which a T i y ≤ b i is violated.

we pay a cost c i times the degree to which the ith constraint is violated, which mirrors how slack variables are used to represent the hinge loss for SVMs.


Such a family consists of constraints that the sum of the counts of two different label types has to be bounded (a useful example is that there can't be more than one out of "phd thesis" and "journal").

quickly pruning bad constraints can save a substantial amount of training time, and can lead to better generalization.


However, this method also suffers the limitation that its effectiveness depends on the accuracy of the concept mapping results.

directly applying existing weighting strategies might lead to nonoptimal retrieval performance.


One limitation of this representation is that relevance scores are computed based on the matching of terms rather than the meanings.

the system may fail to retrieve the relevant documents that do not contain any query terms.


In particular, our preliminary results show that turning on the disambiguation functionality provided by MetaMap (i.e., returning only the most likely concept for each query) could lead to worse retrieval performance than using all the candidate mappings.

we use the one-to-many mapping results generated by MetaMap, in which each aspect can be mapped to multiple concepts.


However, when converting the text to concepts representation using MetaMap, the occurrences of the concepts are determined by not only the original term occurrences, a good indicator of relevance, but also the number of mapped concepts, which is determined by MetaMap and has nothing to do with the relevance status.

the occurrences of concepts might not be a very accurate indicator of importance of the corresponding query aspect.


In fact, this can also be confirmed by experimental results as reported in Table 5.

we use the concept with the maximum IDF value as the representative concept of all the variants.


For example, in the domain of portable computer reviews the word lightweight is often associated with positive sentiment bearing words such as sleek or compact, whereas in the movie review domain the same word is often associated with negative sentimentbearing words such as superficial or formulaic.

the distributional representations of the word lightweight will differ considerably between the two domains.


We observed that setting r larger than 10 did not result in significant improvements in tagging accuracy, but only increased the train time due to the larger feature space.


Distributional semantics assumes a lexicon of atomic expressions (that, for simplicity, we take to be words), each associated to a vector.

at the single-word level, the problem of generation is solved by a trivial generation-by-synthesis approach: Given an arbitrary target vector, "generate" the corresponding word by searching through the lexicon for the word with the closest vector to the target.


The introduction of latent variables into the log-linear model leads to a nonconvex objective function.

bFGS is not guaranteed to converge to the global optimum, but rather to a stationary point.


Our maximization of the log-likelihood function is not guaranteed to converge to a global optimum.

the quality of the learned parameters may be sensitive to the initialization point.


The updating strategy for w d h,t is derived as The initial vectors w i h,1 cannot be made all zero, since otherwise the l-mode product in Equation (5) would yield all zero φ d h,t (x, y) and the model would never get a chance to be updated.

we initialize the entries of w i h,1 uniformly such that the Frobenius-norm of the weight tensor W is unity.


We would like to observe from the experiments how the amount of training data as well as different settings of the tensor degrees of freedom affects the algorithm performance.

we tried all combinations of the following experimental parameters: Settings Training data (m) Sec.


A tensor-space model is a compact representation of data, and via rank-1 tensor approximation, the weight tensor can be made highly structured hence the number of parameters to be trained is significantly reduced.

this can be regarded as a form of model regularization.compared with the traditional vector-space models, learning in the tensor space is very effective when a large feature set is defined, but only small amount of training data is available.


, x n } would be represented by a different set of factors than those given in Equation 7.

in order for tensor Q to represent the same set of real numbers that P represents, there needs to exist a vector [s d 1 , .


For the unlabeled phrases, the set of possible target translations could be extremely large (e.g., all target language n-grams).

we first generate and fix a list of possible target translations for each unlabeled source phrase.


The exponential dependence of the sizes of these spaces on the length of instances is to blame.

the target phrase inventory from the parallel corpus may be inadequate for unlabeled instances.


When propagating information from the labeled phrases, such candidates will obtain no probability mass since e 6 = e 0 .

due to the setup of the problem, LP naturally biases away from translation candidates produced during the generation step ( §2.1).


8 However, over all metrics and all language pairs, DR-LEX is able to obtain an average improvement in correlation of +.035, which is remarkably higher than that of DR.

we can conclude that at the system-level, adding discourse information to a metric, even using the simplest of the combination schemes, is a good idea for most of the metrics, and can help to significantly improve the correlation with human judgments.


However, these earlier studies focused on the ngram translation models, where the translation probability of a phrase or a sentence is decomposed as a product of n-gram probabilities as in a standard n-gram language model.

it is not clear how their approaches can be applied to the phrase translation model 1 , which is much more version of such a model can be trained efficiently because the factor models used by Son et al.


4 The baseline SMT needs to be reasonably good in the sense that the oracle BLEU score on the generated n-best introducing a new feature ℎ +1 and a new feature weight +1 .

the new feature is defined as the phrase-based SMt system, into which the CPtM is incorporated, is parameterized by ( , ), where is a vector of a handful of parameters used in the log-linear model of (1), with one weight for each feature; and is the projection matrices used in the CPtM defined by (2) and (3).


(7) suggests that sim θ (x , x )/ θ and ( , ) can be computed separately, thus making the computation cost of the former term only depends on the number of phrase pairs in the phrase table, but not the size of training data.

the training method described here can be used on larger amounts of training data with little difficulty.


Unlike CPTM, MRFp is a linear model that simply treats each phrase pair as a single feature.

although both are trained using the  Table 2: BLEU results for the French to English task using translation models and systems built on the WMT 2012 data set.


It utilizes a se-ries of deterministic shift-reduce decisions to construct syntactic trees.

it runs in linear time and can take advantage of arbitrarily complex structural features from already constructed subtrees.


After integrating the POS tagging step into the parsing process, our JointParsing system improved the POS tagging accuracy to 94.8% and parsing F 1 to 85.8%, which are significantly better than the Pipeline system.

the joint parsing approach is much more effective for transition-based constituent parsing.


The case frame only with the object(s) proves the other to be valid without the preposition case(s).

these preposition cases are recognized as optional (e.g., [go Subj:{PERSON} Prep dobj:{shopping} (Prep at:{market}) ]).


Our model generates an ordered sequence x although we do not observe its order.

each mention x has latent position x.i (e.g., x 729 .i = 729).


It is difficult to draw exact samples at steps 1 and 2.

we sample i or z from a simpler proposal distribution, but correct the discrepancy using the Independent Metropolis-Hastings (IMH) strategy: with an appropriate probability, reject the proposed new value and instead use another copy of the current value (Tierney, 1994).


For example, if we want to predict whether a paper will be cited or not (Yogatama et al., 2011), the model can perform better if it knows beforehand the collections of words that represent certain themes (e.g., in ACL papers, these might include machine translation, parsing, etc.).

the model can focus on which topics will increase the probability of getting citations, and penalize weights for words in the same topic together, instead of treating each word separately.


Additionally, the regularizer will penalize words in each of the other three groups collectively.

if (for example) ligament is deemed a useful feature for classifying an article to be about sports, then the other words in that topic will have a smaller effective penalty for getting nonzero weights-even weights of the opposite sign as w ligament .


Now let us consider an approximate form of PP 1 (W ′ ) in Theorem 3.

for further discussion, we define the last part ofPP 1 (W ′ ) as follows: Since W ′ = δW holds for an appropriate ratio δ, we have when W is sufficiently large, we can use δ holds for any ratio δ : 0 < δ < 1.


Our theory only assumes that the corpus is generated on the basis of Zipf's law.

we can use a simple model where k-grams are calculated from a random word sequence based on Zipf's law.


If theoretical perplexityPP has a similar tendency as real perplexity PP on a log-log graph, i.e., ln PP(W ′ ) ≈ lnPP(W ′ ) + c for some constant c, we can approximate its deterioration rate as PP(W ′ )/PP(W ) ≈ exp (lnPP(W ′ ) + c)/ exp (lnPP(W ) + c) = PP(W ′ )/PP(W ).

we can use TheoryAve as a heuristic function for estimating the perplexity of topic models.


For example, in the organization domain, we may be more interested in extracting relations such as lo-catedIn (between a company and a location) and founderOf (between a company and a person), whereas in the person domain we may be more interested in extracting relations such as liveIn (between a person and a location) and workAt (between a person and a company).

although the two domains may have the same set of relations, they probably have different marginal distributions on the relations.


Because not-a-relation is a background or default relation type in the relation classification task, and because it has rather high variation when manifested in natural language, we have found it difficult to obtain a distance metric W that allows the not-a-relation samples to form clusters naturally using transductive inference.

we introduce the first clause to assign the not-a-relation label to a sample when there is no strong evidence for any of the positive relation types.


For each target domain, we used k ∈ {1, 3, 5} different source domains chosen randomly from the remaining domains.

the relevance of the source domains to the target domain varies from experiment to experiment.


Furthermore, the confidence scores which MultiR outputs are not normalized to the same scale, which brings us difficulties in setting up a confidence threshold to select the candidates.

we only use the top one result as the candidate since including top two predictions without thresholding the confidences performs bad, indicating that a probabilistic sentence-level extractor is more suitable for our framework.


However, the incomplete knowledge base does not contain the corresponding relation instance (Senate-of(Barack Obama, U.S.)).

the distant supervision paradigm may generate incomplete labeling corpora.


According to Formula (1), Z * ∈ R (n+m)×(d+t) can be represented as [X * , WX * ] instead of [X * , Y * ], by explicitly modeling the bias vector b.

this convex optimization model is called DRMC-b, where µ and λ are the positive trade-off weights.


Translations without grammatical cohesion is hard to read, mostly due to loss of cohesive and transitional expressions between two sentence fragments.

generating transitional expressions is necessary for achieving grammatical cohesion.


For example, when translating the last child of a parallel relation, we always use word "and" as the transitional expression seen in Figure 3, but we will not use it for the first child of a parallel relation.

in the training process we just keep the information of relationships and children's positions when converting  Figure 3: An example of modifying translation rules.


If MT proposals are used properly, post-editing can increase translators productivity and lead to significant cost savings.

it is beneficial to provide MT confidence estimation, to help the translators to decide whether to accept MT proposals, making minor modifications on MT proposals when the quality is high or translating from scratching when the quality is low.


First, existing approaches to MT quality estimation rely on lexical and syntactical features defined over parallel sentence pairs, which includes source sentences, MT outputs and references, and translation models (Blatz et al., 2004;Ueffing and Ney, 2007;Specia et al., 2009a;Xiong et al., 2010;Soricut and Echihabi, 2010a;Bach et al., 2011).

when the MT quality estimation model is trained, it can not be adapted to provide accurate estimates on the outputs of document-specific MT models.


The input documents are automatically segmented into sentences, which are also called segments.

in the rest of the paper we will use sentences and segments interchangeably.


The features for quality estimation should reflect the complexity of the source sentence and the decoding process.

we conduct syntactic analysis on the source sentences, extract features from the decoding process and select the following 26 features: • 17 decoding features, including phrase translation probabilities (source-to-target and target-to-source), word translation probabilities (also in both directions), maxent probabilities 1 , word count, phrase count, distor-tion probabilities, as well as a set of language model scores.


When we use the cross-document models for prediction, the correlation is significantly worse (the details are discussed in section 6.1).

it is necessary to build a QE regression model that's robust to different document-specific translation models.


Finally, we store trie-node children as sorted vectors instead of hash maps to reduce memory footprint.

we are able to load a trie encoding 400M input dependency parses, 170M distinct nodes and 48M distinct sentence structures in under 10GB of RAM.


5 Note that there is no good translation of ROUGE for hierarchical summarization.

we simply use the traditional ROUGE metric, which will not capture any of the hierarchical format.


There are fewer events which talk about the previous storm.

temporal information does assist in identifying which sentences are more relevant to the final summary.


However it is not trivial for the lexically-motivated MMR algorithm to detect that events like "passed", "uprooted" or "damaged" are in fact repetitive.

we propose further penalizing the score of s if it contains events that happen in similar time spans as those contained in sentences within S. We refer to this as TIMEMMR.


This use of the metrics would consider agreement on categories such as "tokens whose head is token number 24", which is obviously not a linguistically informative category.

we have to reject this way of assessing the reliability of dependency syntax annotation.


3 When comparing syntactic trees, we only want to compare dependency relations or non-terminal categories.

we remove the leaf nodes in the case of phrase structure trees, and in the case of dependency trees we compare trees whose edges are unlabelled and nodes are labelled with the dependency relation between that word and its head; the root node receives the label .


Synthetic experiments do not always fully reflect real-world behaviour, however.

we will also evaluate our metrics on real-world interannotator agreement data sets.


The behaviour of α on the other hand is more complex, with structural noise being penalised harder than perturbations of the labels.

the similarity of LAS and α plain is not at all assured when the amounts of structural and labelling disagreements differ.


To extract hypernym lemmas, we draw on the notion of copula, that is, the relation between the complement of a copular verb and the copular verb itself.

we apply the Stanford parser (Klein and Manning, 2003) to the definition of a page in order to extract all the dependency relations of the sentence.


Overall, the final taxonomy T C achieves 85.80% precision, 83.40% recall and 97.20% coverage on our dataset.

page taxonomy improvement of phase 2, 141,105 additional hypernymy links were also added to the page taxonomy, resulting in an overall 82.99% precision, 77.90% recall and 92.10% coverage, with a non-negligible 3% boost from phase 1 to phase 2 in terms of recall and coverage on our Wikipedia page dataset.


Unfortunately Freebase does not contain an exact relation called brother, but instead sibling.

further inference (i.e., brother ↔ male sibling) has to be made.


For instance, for common celebrity gossip questions like who cheated on celebrity A, it is hard for a system to find the Freebase relation celebrity.infidelity.participant as the target relation if it had not observed this pattern in training.

assuming there is an alignment model that is able to tell how likely one relation maps to the original question, we add extra alignment-based features for the incoming and outgoing relation of each node.


This simple example points out that every part of the question could change what the question inquires eventually.


Then we computed how much and how well the answer relation was triggered by ReverbMapping and CluewebMapping.

for the question, who is the father of King George VI, we ask two questions: does the mapping, 1.


WEBQUESTIONS not only has answers annotated, but also which Freebase topic nodes the answers come from.

we evaluated the ranking of retrieval with the gold standard annotation on TRAIN-ALL, shown in Table 3.


(2013) also used ClueWeb indirectly through ReVerb).

we took out the word overlapping and CluewebMapping based features, and the new F 1 on TEST was 36.9%.


Like any language model, a RNNLM estimates the probability of observing a word given the preceding context, but, in this process, it learns word embeddings into a latent, conceptual space with a fixed number of dimensions.

related words tend to have vectors that are close to each other in this space.


We adapted them to this dataset by weighing each answer by its overlap with gold answers, where overlap is measured as the highest F1 score between the candidate and a gold answer.

p@1 reduces to this F1 score for the top answer.


Expected Output: In the example shown in Figure 2, the output would be as follows: The goal of multiple sequence alignment is to find an alignment that maximizes some overall alignment score.

in order to align event sequences, we need to compute scores corresponding to cross-narrative medical event coreference resolution and cross-narrative temporal relations.


In this case, the joint score considers the probability of the temporal relation before and no coreference.

the coreference and temporal relation scores can be leveraged for aligning sequences of medical events.


The WFST composition operation allows the outputs of one WFST to be fed to the inputs of a second WFST or FSA.

we build our final machine by composing the three sub-machines as, where i = c or i = c + t. This gives us a combined weighted graph by mapping the output symbols of the first medical event sequence to the input symbols of the second medical event sequence.


This allows accommodating constraints from across multiple sequences and generates a more accurate best path.

this method generates more accurate alignments when we have more than two sequences to be aligned.


The time complexity of aligning N sequences of length L is O(2 N L N ) (Wang and Jiang, 1994).

for MSA using dynamic programming, we use a heuristic method where we combine pairwise alignments iteratively starting with the latest narrative and progressing towards the oldest narrative.


Based on the SVO triple, the meaning of a fact candidate can be unclear and ambiguous.

we first determine the semantics of a fact candidate before computing its truthfulness.


Thus, equation 2 can be reformulated as µ = M µ, where µ is the co-mention score vector and M is a Markov matrix which is stochastic, irreducible and aperiodic.

a power method will converge to a solution in a similar manner to PageRank.


Questions are multiple choice, with 4 options per question.

from each question, we created one fact candidate and 3 alternative candidates.


FactChecker is insensitive to this parameter when λ is varied from 0.2 to 0.6.

lambda can be robustly chosen within this range.


This is due to the fact that at the core of iterative voting is still the assumption that fact candidates mentioned in many sources are more likely to be true.

for both majority vote and iterative voting, when mention frequencies of various alternatives are the same, accuracy suffers.


For example, the assignment ∀i, j, y ij = ON might get a high score, but would not be a valid output of the model.

we need to impose a structural constraint to ensure that such illegal variable assignments are assigned 0 probability by the model.


We encode this in our factor graph setting using a single global factor T (shown as a large red square in Figure 2) with the following scoring function: φ T (y) = 1 y forms a legal taxonomy tree 0 otherwise Model.

for a given global assignment y, let Note that by substituting our model's factor scoring functions into Equation 1, we get: our model has the form of a standard loglinear model with feature function f .


Note that finding taxonomy trees is a structurally identical problem to directed spanning trees (and thereby non-projective dependency parsing), for which belief propagation has previously been worked out in depth (Smith and Eisner, 2008).

we will only briefly sketch the procedure here.


7 The main difference here from the edge factors is that the sibling factors are symmetric (in the sense that S ijk is redundant to S ikj ) and hence the patterns are undirected.

for each term pair, we first symmetrize the collected Web n-grams and Wikipedia patterns by accumulating the counts of symmetric patterns like rats or squirrels and squirrels or rats.


Next, we modify the objective function in Eq.

1 to be the following: where the new objective function consists of a sum of L×M 2 terms, each corresponding to a different combination of inside and outside features.


For example, as we see in §3.2 we will define the distance d(i, j) to be a function of the covariance ma .

if v i and v j are both observed variables, the distance can be directly computed from the data.


where ν x (γ), defined in the supplementary, is a function of the underlying distribution over the tag sequences x and the kernel bandwidth γ.

the sample complexity of our approach depends on the dimensionality of the latent and observed states (m and p), the underlying singular values of the cross-covariance matrices (σ * (x)) and the difference in the cost of the true tree compared to the cost of the incorrect trees ( (x)).


When two word tokens contain the same consonant frame but different vowels (i.e., minimal pairs), the model is more likely to categorize those two vowels together.

the model has trouble distinguishing minimal pairs.


We assume further that as the child learns the language, she will begin to associate specific words with each topic as well.

in the TLD model, the words used in a situation are topic-dependent, implying meaning, but without pinpointing specific referents.


Analyses of filler-gap dependencies usually involve complex syntactic rules or heuristics; however recent results suggest that filler-gap comprehension begins earlier than seemingly simpler constructions such as ditransitives or passives.

this work models filler-gap acquisition as a byproduct of learning word orderings (e.g.


Position relative to verb Probability Position relative to verb Figure 2: Visual representations of (Left) the initial model's expectations of where arguments will appear, given the initial parameters in Table 2 and (Right) the converged model's expectations of where arguments will appear.

the initial model conditions (see Figure 2) are most likely to realize an SVO ordering, although it is possible to obtain SOV (by sampling a negative number from the blue curve) or even OSV (by also sampling the red curve very close to 0).


When imperatives are filtered out of the training corpus, the symmetric model obtains a worse BIC fit than a model that lacks the non-canonical subject Gaussian.

if one makes the assumption that imperatives are prosodically-marked for learners (e.g.


The violations on a given output form only affect probabilities within its paradigm.

learned constraints are consistent within paradigms, but across paradigms, the same constraint may serve different purposes.


Instead, it learns that M1 has the same violations as HARMONY, which is the highest-weighted constraint that distinguishes between candidates in these paradigms.

in the high-vowel paradigms, M1 serves as *I, while in the low/mid-vowel paradigms, it serves as HAR- The lower-weighted M2 is defined noisily, as the higher-ranked M1 makes some values of M2 inconsequential.


In all four paradigms, the model learns that the RTR-RTR candidate violates M2 and the ATR-ATR candidate does not; this appears to be the model's attempt to reinforce a pattern in the lowest-ranked faithfulness constraint (PARSE[atr]), which the ATR-ATR candidate never violates.

while the IBPOT constraints are not identical to the phonologically standard ones, they reflect a version of the standard constraints that is consistent with the IBPOT framework.


There are two main goals of developing this classifier: (1) accurately predicting the labels of data points and ranking them based on prediction confidence, so that the most likely errors can be effectively identified; (2) requiring less time on training, so that the saved time can be spent on correcting more labeling errors.

we aim to build a classifier that is both accurate and time efficient.


Average Precision (AP) is the average of the algorithm's precision at every position in the confidence ranked list of results where a true emotional document has been identified.

aP places extra emphasis on getting the front of the list correct.


Because the translation of a source constituent might result in multiple discontinuous blocks, there can be several ways to describe or group the reordering patterns.

we design two general constituent reordering sub-models.


Of all the semantic role pairs, 44% are in the same CFG rules, indicating that this part of semantic reordering has overlap with syntactic reordering.

the PAS model has fewer opportunities to influence reordering.


This output translation is the result of the combined translation and editing stages.

our method operates over a heterogeneous network that includes translators and post-editors as well as the translated sentences that they produce.


In the following sections, we evaluate each of our methods by calculating BLEU scores against the same four sets of three reference translations.

each number reported in our experimental results is an average of four numbers, corresponding to the four possible ways of choosing 3 of the 4 reference sets.


We can thus formulate the interpolation step of modified Kneser-Ney smoothing using our notation asP MKN  .

our skip n-grams correspond to n-grams of which we only use k words, after having applied the skip operators Interpolation with lower order models is motivated by the problem of data sparsity in higher order models.


On the other hand, once such assumptions are removed, another problem arises -they might be prone to errors, and suffer from the overfitting issue.

coping with the tradeoff between expressiveness and overfitting, seems to be rather important in statistical approaches that capture stochastic dependency.


Let the corresponding marginal cumulative distribution functions of the random variable be Then, if the marginal functions are continuous, there exists a unique copula C, such that Furthermore, if the distributions are continuous, the multivariate dependency structure and the marginals might be separated, and the copula can be considered independent of the marginals (Joe, 1997;Parsa and Klugman, 2011).

the copula does not have requirements on the marginal distributions, and any arbitrary marginals can be combined and their dependency structure can be modeled using the copula.


However, this might not be practical at all: in image processing, the "cloud" pixel of a pixel showing the blue sky of a picture are more likelihood to co-occur in the same picture; in natural language processing, the word "mythical" is more likely to co-occur with the word "unicorn", rather than the word "popcorn".

by modeling the correlations among marginal CDFs, the copula model has gained the insights on the dependency structures of the random variables, and thus, the performance of the regression task is boosted.


However, these semantic resources are limited in its scope and domain, and their manual construction is knowledge intensive and time consuming.

many researchers * Email correspondence.


Actually, x, y and z are unambiguous as the hypernyms of a certain entity.

g should be a directed acyclic graph (DAg).


Examples Additionally, their experiment results have shown that the Skip-gram model performs best in identifying semantic relationship among words.

we employ the Skip-gram model for estimating word embeddings in this study.


Note that mapping one hyponym to multiple hypernyms with the same projection (Φx is unique) is difficult.

the pairs with the same hyponym but different hypernyms are expected to be clustered into separate groups.


Also, it is difficult to construct formal ontologies of properties and relations that have broad coverage, and semantically parsing sentences into logical expressions utilizing such an ontology is very difficult.

current semantic parsers are mostly restricted to quite limited domains, such as querying a specific database (Kwiatkowski et al., 2013;Berant et al., 2013).


PSL does not work very well "out of the box" for STS, mainly because Lukasiewicz's equation for the conjunction is very restrictive.

we use a different interpretation for conjunction that uses averaging, which requires corresponding changes to the optimization problem and the grounding technique.


However, humans find these sentences to be quite similar.

we introduce a new averaging interpretation of conjunction that we use for the hypothesis H. The truth value for a conjunction is defined as This averaging function is linear, and the result is a valid truth value in the interval [0, 1], therefore this change is easily incorporated into PSL without changing the complexity of inference which remains a linear-programming problem.


However, this is a combinatorial process that can easily result in an explosion in the size of the final network.

pSL employs a "lazy" approach to grounding that avoids the construction of irrelevant groundings.


Input : GndConst(a, g, v): takes an atom a, grounding g for a, and variable v, and returns the constant that substitutes v in g Input: gnd limit: limit on the number of sort Const(v i ) on scores, descending 6: end for 7: return For all v i ∈ V , take the Cartesianproduct of the sorted Const(v i ) and return the top gnd limit results trivially satisfied (I(r) = 1) since the truth value of the antecedent is zero.

its distance to satisfaction is also zero, and it can be omitted from the ground network without impacting the result of MPE inference.


However, this produces an intractably large network.

we developed a heuristic approximate grounding technique that generates a subset of the most impactful groundings.


The purpose of such a model is three-fold: i) to cover the content of query information optimally; ii) to generate a more readable and grammatical sentence; and iii) to favor strong connections between the concepts.

the final ranking score of path P is calculated over the normalized scores as: Where α, β and γ are the coefficient factors to tune the ranking score and they sum up to 1.


Firstly, they change over time, and secondly they can be dependent on or independent of each other.

when generating feedback, we need to take into account all variables simultaneously in order to capture potential dependencies and provide more effective and useful feedback that is relevant to the students.


Instead of dealing with this task in a hierarchical way, where the algorithm will first learn whether to talk about a factor and then to decide how to refer to it, we transformed the task in order to reduce the learning steps.

classification can reduce the decision workload by deciding either in which way to talk about it, or not to talk about a factor at all.


In the LP relaxation, x i and z ij are redefined as real-valued variables in [0, 1], potentially resulting in fractional values for dependency and token indicators.

the commodity flow network is able to establish connectivity but cannot enforce a tree structure, for instance, directed acyclic structures are possible and token indicators x i may be partially be assigned to the solution structure.


They help to build a system that is more robust across domains.

rather than trying to build a specialized system for every new target domain, as it has been done in most prior work on domain adaptation (Blitzer et al., 2007;Daumé, 2007), the domain adaptation problem boils down to finding a more robust system (Søgaard and Johannsen, 2012;Plank and Moschitti, 2013).


To improve the speed computation of T K, we consider pairs of nodes (n 1 , n 2 ) belonging to the same tree level.

given H, the height of the STRUCT trees, where each level h contains nodes of the same type, i.e., chunk, POS, and lexical nodes, we define SHTK as the following 5 : where N h T 1 and N h T 2 are sets of nodes at height h. The above equation can be applied with any ∆ function.


The largest group of errors are implicit sentiments.

some comments do not contain any explicit positive or negative opinions, but provide detailed and well-argumented criticism, for example, this phone is heavy.


In contrast, a scientific paper typically has at least 10 keyphrases and hundreds of candidate keyphrases, yielding a much bigger search space (Hasan and Ng, 2010).

it is harder to extract keyphrases from scientific papers, technical reports, and meeting transcripts than abstracts, emails, and news articles.


However, for a long document, the resulting list of candidates can be long.

different pruning heuristics have been designed to prune candidates that are unlikely to be keyphrases (Huang et al., 2006;Kumar and Srinathan, 2008;El-Beltagy and Rafea, 2009;You et al., 2009;Newman et al., 2012).


As we can see, the word Olympic(s) has a significant presence in the document.

many systems not only correctly predict Olympics as a keyphrase, but also erroneously predict Olympic movement as a keyphrase, yielding overgeneration errors.


100-meter dash is mapped to the topic Sprint of type Sports in the Sports domain, whereas gold medal is mapped to a topic with the same name of type Olympic medal in the Olympics domain.

we can relate 100-meter dash to Ben Johnson via the Sports domain (i.e., they belong to different types under the same domain).


The TPP corpus has not been cleaned in the same manner as the FN and the OEC corpora.

we see many cases which are more difficult to parse and hence, from which to generate feature sets.


We believe this provides a truer real-world picture of the complexities of preposition behavior.

in the Tag Instances drop-down box, we have included an option to tag a sentence as x, to indicate that it is not a valid instance.


However, in the TPP corpus, only 3 out of 750 instances were identified for this sense (0.4 percent).

while FrameNet culled a large number of instances which had these frame element realizations, these instances do not appear to be representative of their occurrence in a random sample of of uses.


(2007) Medical FR/JP 693,666/807,287 words Otero (2007) European Parliament SP/EN 14/17 million words Ismail and Manandhar (2010) European Parliament EN/SP 500,000/500,000 sentences Bouamor et al.

2013  "in relation to parallel corpora, it is more likely for comparable corpora to be designed as general balanced corpora.".


After a manual selection, we only kept the documents which were relative to the medical domain.

65 French documents were extracted (about 257,000 words).


We kept only i) the French single words which occur more than four times in the French part and ii) the English single words which occur more than four times in each English part i 7 .

of filtering, 169 French/English single words were extracted for the breast cancer corpus and 244 French/English single words were extracted for the diabetes corpus.


More precisely, our different experiments show that using an unbalanced specialized comparable corpus always improves the quality of word translations.

the MAP goes up from 29.6% (best result on the balanced corpora) to 42.3% (best result on the unbalanced corpora) in the breast cancer domain, and from 16.5% to 26.0% in the diabetes domain.


be low, the majority annotation of an item may be correct.

in Table 1, we calculate the percentage agreement of the aggregated annotations with the gold standard annotations for approving valid relations (true positives; Col. 5), rejecting invalid relations (true negatives; Col. 6), and for both combined (Col. 7).


The second limitation, related to the ineffective weighting of terms via the the tf -idf scheme, seems to be addressed by the fact that the MaxEnt model assigns a weight to each feature (in our case, POS bigrams) on a per-class basis (in our case, score group), by taking every instance into consideration.

a MaxEnt model has an advantage over the model described in 4.1 in that it uses four different weight schemes (one per score level) and each scheme is optimized for each score level.


The performance gain of Base+cos4 over Base, however, is not statistically significant at level = 0.01.

the inclusion of the MaxEntbased measure of syntactic complexity results in improved agreement between machine and human scores compared to the state-of-the-art model (here, Base).


In contrast, the AP English data exhibits a correlation of ρ = 0.93 (Church and Gale, 1999).

the deviation in the Tagalog corpus is more pronounced, i.e.


The results from full language packs in Table 3 also show that there is a reverse interaction of morphological complexity and the effect of blindly adding more data.

for morphologically rich languages, adding more data is less effective than for languages with poor morphology.


If our neural network has only one hidden layer and is self-normalized, the only remaining computation is 512 calls to tanh() and a single 513-dimensional dot product for the final output score.

6 only ∼3500 arithmetic operations are required per n-gram lookup, compared to ∼2.8M for self-normalized NNJM without precomputation, and ∼35M for the standard NNJM.


14 However, the n-grams created for the NNJM can be shared with the Kneser-Ney LM, which reduces the cost of that feature.

the total cost increase of using the NNJM+NNLTM features in decoding is only ∼0.01 seconds per source word.


However, we have demonstrated that we can obtain 50%-80% of the to-tal improvement with only one model (S2T/L2R NNJM), and 70%-90% with only two models (S2T/L2R NNJM + S2T NNLTM).

the one and two-model conditions still significantly outperform any past work.


However, if we fix any two sets of parameters, for example, if we fix V and W , then the combined score S γ (x, y) will be a linear function of both θ and U .

the objective will be jointly convex with respect to θ and U and could be optimized using standard tools.


Note that φ h , φ m , φ h,m , and φ h→m are typically very sparse for each word or arc.

du and dθ are also sparse and can be computed efficiently.


We should note that since our model parameter A is represented and learned in the low-rank form, we only have to store and maintain the low-rank projections U φ h , V φ m and W φ h,m rather than explicitly calculate the feature tensor φ h ⊗φ m ⊗φ h,m .

updating parameters and decoding a sentence is still efficient, i.e., linear in the number of values of the feature vector.


CoSimRank can be used to compute many variations of basic node similarity -including similarity for graphs with weighted and typed edges and similarity for sets of nodes.

coSimRank has the added advantage of being a flexible tool for different types of applications.


Whereas SimRank sets each of these entries back to one at each iteration, CoSimRank adds one.

when computing the two similarity measures iteratively, the diagonal element (i, i) will be set to 1 by both methods for those initial iterations for which this entry is 0 for cAS (k−1) A T (i.e., before applying either max or add).


For the more typical case that we only want to compute a fraction of all similarities, we have recast the global SimRank formulation as a local CoSimRank formulation.

time and space complexities are much improved.


We propose CoSimRank as an efficient algorithm for computing the similarity of nodes in a graph.

we compare against the two main methods for this task in NLP: SimRank and extensions of PageRank.


On the contrary, the first time a learner is exposed to a new object, the linguistic information available is likely also very limited.

in order to consider vision-to-language mapping under more plausible conditions, similar to the ones that children or robots in a new environment are faced with, we next simulate a scenario akin to fast mapping.


During training, this cross-modal vocabulary is used to induce a projection function (Section 4.4), which -intuitively -represents a mapping between visual and linguistic dimensions.

this function, given a visual vector, returns its corresponding linguistic representation.


Once associations are constructed, we mark tokens in x and c that were not part of any association, and extract deletion features for their lemmas and POS tags.

we learn that deleting pronouns is acceptable, while deleting nouns is not.


higher, as this utterance is also in the music domain.

we combine the two models to benefit from their complementary nature.


We surmise this is because questions in FREE917 were generated by annotators prompted by Freebase facts, whereas questions in WEBQUESTIONS originated independently of Freebase.

word choice in FREE917 is often close to the generated Freebase descriptions, allowing simple baselines to perform well.


In addition, akin to the first context function, we also added all dependency labels to the context set.

for this context function, the block cardinality k was the sum of the number of scanned gold dependency path types and the number of dependency labels.


We believe that the WSABIE EMBEDDING model performs better than the LOG-LINEAR EMBEDDING baseline (that uses the same input representation) because the former setting allows examples with different labels and confusion sets to share information; this is due to the fact that all labels live in the same label space, and a single projection matrix is shared across the examples to map the input features to this space.

the WSABIE EMBEDDING model can share more information between different examples in the training data than the LOG-LINEAR EMBEDDING model.


In the context of machine translation, such different meanings normally produce different target translations.

a natural assumption is that word sense disambiguation (WSD) may contribute to statistical machine translation (SMT) by providing appropriate word senses for target translation selection with context features (Carpuat and Wu, 2005).


As LDA needs to manually specify the number of senses (topics), a better idea is to let the training data automatically determine the number of senses for each word type.

we resort to the HDP, a natural nonparametric generalization of LDA, for the inference of both sense clusters and the number of sense clusters following Lau et al.


Figure 3 (b) shows that both RRN s and F F N N s work for such simpler alignments.

the RNN-based model has less effect on French-English word alignment than Japanese-English word alignment, as indicated in Table 2.


Table 4 demonstrates that the proposed RNNbased model outperforms IBM 4 trained from the unlabeled 40 K data by employing either the 1 K labeled data or the 9 K unlabeled data, which is less than 25% of the training data for IBM 4.

the SMT system using RN N u+c trained from a small part of training data can achieve comparable performance to that using IBM 4 trained from all training data, which is shown in Table 3.


Every bidirectional alignment with finite objective score must align exactly one word in e to each word in f, encoding a permutation a.

moreover, each possible permutation has a finite score: the negation of the total distance to traverse the N cities in order a under distance c. solving such a bidirectional alignment problem would find a minimal Hamiltonian path of the TSP encoded in this way, concluding the reduction.


The major reason is that the basic semantic unit of Chinese language is actually word (tough vaguely defined) which is usually composed of several characters.

the conditional probability between characters does not make much sense.


As we observe on T that the average pinyin word length is 5.24, then typo rate in the experiment of (Zheng et al., 2011a) can be roughly estimated as: 775 + 85 × 2 11968 × 5.24 = 1.51%, which is similar to the conclusion on English.

we generate corpora from D with typo rate of 0% (0-P), 2% (2-P), and 5% (5-P) to evaluate the system.


From our statistics on T, with 2% randomly generated typos, P r(L(S ′ , S) < 2) = 99.86%.


To select multi-word spans today, mobile devices require an intricate series of gestures that results in cumbersome user experiences 1 .

there is an opportunity to reinvent the way users select text in such devices.


For each paragraph, we request the judges to select complete phrases for which they would like to "learn more in resources such as Wikipedia, search engines and dictionaries", i.e., our true user intended selections.

of this exercise, we obtain 13,681 judgments, corresponding to 4,067 unique intended selections.


While each of the essays in our data set was previously annotated with these thesis clarity errors, in a realistic setting a prompt adherence scoring system will not have access to these manual error labels.

we first need to predict which of these errors is present in each essay.


However, an exact solution to this optimization problem is computationally expensive, as there are too many (7 13 ) possible combinations of c values to exhaustively search.

we find a local maximum by employing the simulated an- 4 Since our regressor assigns each essay a real value rather than an actual valid score, it would be difficult to obtain a reasonable S1 score without rounding the system estimated score to one of the possible values.


End-users of such a lexicon may not wish to deal with Word Sense Disam-biguation (WSD), which is known to be often too noisy to be incorporated into the pipeline with respect to other NLP tasks.

researchers often would need to aggregate labels across different senses to derive the word-level label.


A Complexity analysis Most demanding component of Algorithm 1 is the iterative message passing over the edges (lines 10-14), with time complexity O(ml 2 r), where m = |E| is the number of edges in the connotation graph, l = |L|, the classes, and r, the iterations until convergence.

often, l is quite small (in our case, l = 2) and r m. running time grows linearly with the number of edges and is scalable to large datasets.


The sentiment lexicons we use as gold standard are small, compared to the size (i.e., number of words) our graphs contain.

we first find the overlap between each graph and a senti-  ment lexicon.


In the neural network, the distributed representation of higher layer are interpreted as features describing the input.

we utilize the continuous vector of top layer to predict the sentiment distribution of text.


For example, as shown in figure 1, the phoneme /r/ surfaces as [X] or [K] depending on whether or not the next sound is a voiceless obstruent.

the lexical item /kanar/ surfaces as [kanaX] or [kanaK].


A more realistic categorization (like the HMM-based one), will naturally tend to minimize within-category distance, and maximize between-category distance.

we will have less overlap, making the chances of the pair to generate a lexical pair smaller.


A more intuitive, but less ambitious, assumption is to restrict the scope of top-down cues to contrasts that do generate lexical variation (lexical alternants or true minimal pairs).

they remain completely agnostic to the status of invisible pairs.


For example, the amount of time required to read a word varies as a function of how predictable that word is (McDonald and Shillcock, 2003).

failure of a language model to predict human performance reveals a mismatch between the language model and the human language model, i.e., bias.


In situated human-robot dialogue, humans and robots have mismatched capabilities of perceiving the shared environment.

referential communication between them becomes extremely challenging.


A typical two-step coreference algorithm uses mentions generated by the best possible mention detection algorithm as input to the coreference algorithm.

ideally one would want to score the two steps independently of each other.


These misunderstandings are caused partially by the lack of illustrative examples to show how a metric is computed on predicted mentions not aligned perfectly with key mentions.

we provide detailed steps for computing all four metrics on a representative example.


(2013)), it is more complex than the first for sentiment annotation.

independent of how good the annotator is, there are sentences which will be perceived to be more complex than others.


This is to prevent fatigue over a period of time.

each annotator participates in this experiment over a number of sittings.


According to this measure, citations are most similar to the books domain.

it is not surprising that training on books performs well on citations, and intuitively, among the domains in the Amazon dataset, a book review is most similar to a scientific citation.


Among the great varieties of topics, people in Twitter tend to express their opinions for the brands, celebrities, products and public events.

it attracts much attention to estimate the crowd's sentiments in Twitter.


This issue becomes more serious in our setting of single-system DA where we have a single source domain with multiple dissimilar target domains and an automatic system able to recognize entity and mention types very well in different domains may not be available.

following the settings of Plank and Moschitti (2013), we will only assume entity boundaries and not rely on the gold standard information in the experiments.


In the PTK, a subtree may or may not expand any child in a production rule, while maintaining the ordering of the child nodes.

it generates a very large but sparse feature space.


C(n 1 , n 2 ) counts the number of common descending paths rooted at nodes n 1 and n 2 : C(n 1 , n 2 ) can be computed in polynomial time by the following recursive rules: Rule 1: If n 1 and n 2 have different labels (e.g., "DT" versus "NN"), then C(n1, n2) = 0; Rule 2: Else if n 1 and n 2 have the same labels and are both pre-terminals (POS tags), then where term(n) is the terminal symbol under n; Rule 3: Else if n 1 and n 2 have the same labels and they are not both pre-terminals, then: where children(m) are the child nodes of m. As in other tree kernel approaches (Collins and Duffy, 2001;Moschitti, 2006), we use a discount parameter λ to control for the disproportionately large similarity values of large tree structures.

rule 2 becomes: and rule 3 becomes: Note that Eq.


According to Gusfield (1997), an edit transcript (representing the conversion of one string to another) and an alignment are mathematically equivalent ways of describing relationships between strings.

because the edit distance was widely used in this research area and produced good results, we are encouraged to employ orthographic alignment for identifying pairs of cognates, not only to compute similarity scores, as was previously done, but to use aligned subsequences as features for machine learning algorithms.


String alignment is closely related to the task of sequence alignment in computational biology.

to align pairs of words we employ the Needleman-Wunsch global alignment algorithm (Needleman and Wunsch, 1970), which is mainly used for aligning sequences of proteins or nucleotides.


Its main idea is that any partial path of the alignment along the optimal path should be the optimal path leading up to that point.

the optimal path can be determined by incremental extension of the optimal subpaths (Schuler, 2002).


One of our goals is to automatically generate high quality synsets, each of which is a set of cognitive synonyms, for Wordnets having the same structure as the PWN in several languages.

we use the first approach to construct Wordnets.


If a candidate has a higher occurrence count, it has a greater chance to become a correct translation.

the occurrence count of each candidate needs to be taken into account.


In our first extension, we augment the model by incorporating a Markov chain over the vertical offset random variables This means that the if v i differs substantially from v i−1 , a large penalty is incurred.

the model should prefer sequences of v i that vary slowly.


The phrase translates into "a press conference held in Kabul".

we obtained eight pre-ordering rules in total, which can be divided into three dependency relation categories.


However, in English, this kind of word (e.g., "front" in the caption of Figure 2) always occur directly after prepositions, which is to say, in the second position in a prepositional phrase.

we applied a rule plmod : lobj (localizer object) to reposition the dependent word of the plmod relation (e.g., "前" in Figure 2) to the position before the lobj structure (e.g., "美国 大使馆" in Figure 2).


They share up-to-date topics and sometimes quote each other.

external knowledge, such as news, provides rich supplementary information for analysing and mining microblogs.


Compared with the original LDA optimization problem (1), the topic inference problem for microblog (2) follows the idea of document generation process, but replaces topics to be estimated with known topics from other corpus.

parameters to be inferred are only the topic distribution of every microblog.


Every column of A must have at least one non-gap symbol.

the j th column of A indicates an alignment state for the j th position, where the state can have one of the 2 K − 1 possible combinations.


It refers to notions of who(person), where(place), when(time) including what, why and how in a document.

we can assume that named entities(NE) are linguistic features for event detection.


The size that optimized the average Rouge-1(R-1) score across 30 tasks was chosen.

we set tf * idf and KL-distance to 100 and 0.104, respectively.


Finally, our results highlight that the effectiveness of an importance model depends on the genre of the source text.

while a first sentence baseline is very competitive in news summarization, we found that important information tends not to be located in the opening sentences in our data (these tend to provide general, introductory information), but appears later on, when more detailed, specific claims are put forward.


认清) require exact one object argument, while others generally do not.

compared to word-level POS, the character-level POS can produce information for more expressive features during the learning process of a morphological analyzer.


It should be noted that, for single-character words, the difference between adjective and adverb can almost be ignored, because for any of such words that can be used as an adjective, it usually can also be used as an adverb.


The treebank does not have default partition to training and test sets.

from each 10 consecutive sentences, we assign the 9th and 10th to the development set and the test set, respectively.


First, the success of the approach is highly dependent on a well designed coarse label set.

it requires intimate knowledge of the tag set and language.


We explain these results on the basis of the principle developed for the human correlation data: To reflect natural judgments of similarity for comparisons of representations of differing sparseness, α should be tipped toward the sparser representation.

α = .80 works best for high rank target words, because most nearest neighbor candi-MC/RG Wdsm201 Wdsm353 α = .5 opt opt α α = .5 opt opt α α = .5 opt opt α DICE PROD .65 .


While EDUs are textual units for RST, they are too fine grained as textual units for methods of extractive summarization.

the models have tended to select small fragments from many sentences to maximize objective functions and have led to fragmented summaries being generated.


If z j = 0, then in both equations, all terms having z j are zero; If z j = 1, i.e., x j is kept, since it has exactly one head word x k in the compressed sentence, the sum of the terms having z j is w tok j + w dep kj for both equations.

we only need to consider the scores of arcs.


We collect potential abbreviation/full-word pairs by looking for terms that could be abbreviations of full words that occur in the same context.

: the svc/service center heating clng/cooling system dry clng/cleaning system contributes evidence that svc is an abbreviation of service.


Our interest being in difficult, disagreeable cases, we group the instances into 5 bands according to the observed level of disagreement and the tendency in the majority of the annotations.

items with at most two label 1 annotations are clearly semantically new, while those with at least 17 (out of 20) are clearly semantically old.


Exposure to such cases at training time leads the system to forgo handling rare senses that lack distributional evidence, thus leading to misclassification of easy positive cases that exhibit a similar pattern.

stall and market are both used in the sales outlet sense in one of the text.


Previous research has shown that humans find it easier to reason about real-world scenarios than make abstract judgments (Cosmides and Tooby, 1992).

for each feature (e.g., MOVEMENT), we converted the metalinguistic judgment ("Does this verb entail movement on the part of some entity?")


Thus Sally rolled the ball entails that somebody applied force to the ball (namely: Sally), whereas The ball rolled does not.

we investigate the semantics of each verb in each syntactic frame available to it (as described by VerbNet).


Given the sheer scale of the project, datacollection is expected to take several years at least.

data-collection has been broken up into a series of phases.


Even a single verb may have different semantic entailments when placed in different syntactic frames.

calculating consistency of a class must take differing frames into account.


We are currently investigating whether we can achieve better reliability with fewer responses per item by taking into account an individual annotator's history across items, as recent work suggests is possible (Passonneau and Carpenter, 2013;Rzhetsky et al., 2009;Whitehill et al., 2009).

crowd-sourcing VerbNet semantic entailments appears to be both feasible and productive.


Usually, a high quality emotion lexicon play a significant role when apply the unsupervised approaches for fine-grained emotion classification.

* Dingju Zhu is the corresponding author far, most lexicon construction approaches focus on constructing general-purpose emotion lexicons (Stone et al., 1966;Hu and Liu, 2004;Wilson et al., 2005;Dong and Dong, 2006).


For each word w in the vocabulary, we compare the M + 1 values {ϕ i,w is the largest, then the word w is added to the emotion dictionary for the ith i,w is the largest among the M + 1 values, which suggests that the word w is more probably drawn from a nonemotion topic.

the word is considered neutral and not included in the emotion dictionary.


However, the systems are statistically indistinguishable in the document-level tag evaluation (f T ).

the extra NER and coreference machinery may not be justified if the application is document indexing or social media mining (Meij et al., 2012), wherein a KB-driven mention detector may be favourable for other reasons.


(2011) noticed that a multi-goal task may require a user to issue a series of queries, spanning a long period of time and multiple search sessions.

they addressed the problem of modeling and analyzing complex cross-session search tasks.


A complex task consists of several sub-tasks, and each sub-task goal may be composed of a sequence of search queries.

modeling the sub-tasks is necessary for identifying a complex task.


However, users may also simultaneously interleave search for multiple different tasks (MacKay and Watters, 2008;Liu and Belkin, 2010).

identifying task-coherent queries from search sessions is an important issue.


For example a query " 北京機票(Beijing flight ticket)" contains a topic "北京(Beijing)" and an entity "機票(flight ticket)".

we first try to extract task-intrinsic entities from the query set by extracting all common nouns in each of queries.


The precision and coverage rate of top n entities used in our microblog retrieval model We found that using top 5 task-intrinsic entities can achieve the best results.

for each query, we will generate 20 (i.e., ) pseudo queries and we retrieved top 10 microblog posts for each pseudo queries (totally 200 posts for each testing query).


When NE SF are spanned over several rows, these rows are merged into a single one.

we consider an annotation object A D , which is an array with a row for each NE, and columns storing related knowledge.


The stated goal of TempEval-3 (UzZaman et al., 2013) was to focus on relation identification instead of classification, but the training and evaluation data followed the TimeBank approach where only a subset of event pairs were labeled.

many systems focused on classification, with the top system classifying pairs in only three syntactic constructions There were four or five people inside, and they just started firing Ms. Sanders was hit several times and was pronounced dead at the scene.


While a number of different approaches for domain adaptation have been proposed (Pan and Yang, 2010;Søgaard, 2013), they tend to emphasize bag-ofwords features for classification tasks such as sentiment analysis.

many approaches rely on each instance having a relatively large number of active features, and fail to exploit the structured feature spaces that characterize syntactic tasks such as sequence labeling and parsing (Smith, 2011).


We can use similar reasoning to compute the expectation of P. With probability (1 − p), the original features are preserved, and we add the outer-product x i x i ; with probability p, we add the outer-product x i q .

e[P] can be computed as the sum of these terms.


In fact, RCM does not even observe all the words that appear in the training set, so it makes little sense to use the RCM embeddings directly for language modeling.

in order to make fair comparison, for every set of trained embeddings, we fix them as input embedding for word2vec, then learn the remaining input embeddings (words not in the relations) and all the output embeddings using cbow.


In contrast to Algorithm 1, which only selected one segmentation boundary at once, in our new setting there are multiple positions selected at one time.

we need to update our search algorithm to handle this setting.


Obviously the skeleton used in this work can be viewed as a simplified sentence.

the problem is in principle the same as sentence simplification/compression.


The reason is that a sentence pair contains a source language sentence and a target language sentence, while the existing methods are incapable of evaluating the mutual translation probability of sentence pair in the target domain.

we propose novel methods which are based on translation model and language model for data selection.


Here, we employ ngram language 1http://www.nlplab.com/NiuPlan/NiuTrans.YourData.ch.html 2http://www.nlplab.com/NiuPlan/NiuTrans.ch.html#download 3 http://ww.itl.nist.gov/iad/mig/tools model and IBM Model 1 for data selection.

we use the SRI Language Modeling Toolkit (Stolcke, 2002) to train the in-domain 4-gram language model with interpolated modified Kneser-Ney discounting (Chen and Goodman, 1998).


We use our three methods to refine the generaldomain corpus and improve the translation performance in the domain of interest.

we build several contrasting systems trained on refined training data selected by the following different methods.


In Spanish, this mainly constitutes itself as different word endings.

we redefine our word dissimilarity measure by consider words similar, if they agree in at least a number of leading letters (presumably the stem), and may differ in at most a number of trailing letters (presumably the morpheme).


These extracted hypernyms can be treated as article categories.

articles containing the same hypernym are likely to belong to the same category.


In the above example, the following pattern is matched: is NN nsubj cop nn [target] In this pattern, the rightmost leaf is the hypernym target.

we can extract the hypernym "novel" from the previous example.


to changing; the correction "moment ⇒ minute" is itself a single word substitution.

we can build systems to detect corrections which operates in two steps: (1) detecting the basic edits that took place during the revision, and (2) merging those basic edits that address the same error.


Producing better recommendations is even more critical for cold start users to make them continue to use the system.

this paper focuses on how to overcome the cold start problem and obtain a better PLM for cold start users.


This task can be illustrated by considering the following topic derived from social media related to Education: school protest student fee choic motherlod tuition teacher anger polic where the top 10 words ranked by P (w i |t j ) for this topic are listed.

the task is to find the top-n terms which are more representative of the given topic.


The same preprocessing steps were performed on NW.

following a similarity alignment approach we performed the steps oulined in Algorithm 1 for generating the GS topic labels of a topic in TW.


(2010), we model these steps using a conditional loglinear model, p θ (e | C) To increase log p θ (y k | x k ), we must raise the probability of the edits e that were used to transduce x k to y k , relative to competing edits from the same contexts C. This means raising θ • f (C, e) and/or lowering Z C .

log p θ (y k | x k ) depends only on the probabilities of edit arcs in T that appear in x k • T • y k , and the competing edit arcs from the same edit states q C .


An interesting finding is that, although limited in length, the textual information in the search result's metadata contain enough salient terms relevant to the topic to provide reliable estimates of  term importance.

it is not necessary to measure semantic similarity between topic keywords and candidate labels as previous approaches have done.


Josa is used to define nominal cases and modify other phrases, while Eomi is an ending of a verb or an adjective to define a tense, show an attitude, and connect or terminate a sentence.

the Eomi and Josa categorization plays an important role in signaling semantic roles.


The larger the label set is, the greater the chance label ambiguity will dominate the beam.

we introduce a second level of beam specifically for labeled signatures.


6 For example, while "to" is a match for PP-t, its attachment is not, since in Figure 2 it is a child of the "trip" etree (#a5) and in Figure 3 it is a child of the "make" etree (#b3).

our analysis results in an attachment score for every regex.


With a fixed amount of memory available, we cannot allow our list of unique words or phrases to grow over time, even while new topics give rise to novel names of people, places, and terms of interest.

we need an approach that keeps the size of the model constant, but that is geared to what is being discussed now, as compared to some time in the past.


With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events.

it is crucial to extract events from the social streams such as tweets.


However, it is often observed that events mentioned in tweets are also reported in news articles in the same period (Petrovic et al., 2013).

named entities mentioned in tweets are likely to appear in news articles as well.


However, finding the optimal subset of training instances is difficult, as there exist 2 N possible solutions for a relation type with N training instances.

this paper proposes an approximate training instance selection algorithm as follows: 1) Computing the centroid of ith relation type as 2) Finding the set of training instances which can most compactly span the centroid by solving: 3) Ranking all training instances according to their absolute coefficient weight value ; 4) Selecting top p percent ranked instances as final training instances.


Also, Guided DS improves the overall recall by more than 9% absolute (from 30.9% to 39.93%) at a comparable level of precision (24.35% for MIML vs 23.64% for Guided DS), while increases the running time of MIML by only 3%.

our approach outperforms state-of-the-art model for relation extraction using much less labeled data that was used by Zhang et al., (2012) to outper-form logistic regression baseline.


Such cases are quite frequent (∼17%) in the settings of our dataset, described in Section 3.

we define the task of recognizing implied predicate-argument relationships, with illustrating examples in Table 1, as follows.


More concretely, in textual inference the candidate predicate and argument are typically identified, as they are aligned by the RTE system to a predicate and an argument of the Hypothesis.

the only remaining challenge is to verify that the sought relationship is implied in the text.


The metaphoricity values for this experiment were calculated in the same way: the percentage of participants who rated a sentence as highly metaphoric.

this measurement also is based on the idea that more participants will be consciously aware of highly metaphoric sentences, with a third category avail-able to allow an extra distinction to be made.


However, because sentences with the same main verb were forced into a three-way ordering, participants could not, for example, label two of the sentences as equally metaphoric.

it is possible that some of this advantage on the upper bound is a result of the task itself.


These WordNet senses are first mapped to SynSets and then to concepts in the SUMO ontology, using existing mappings (Niles & Pease, 2001, 2003.

each sentence is represented by the SUMO concepts which it contains and each concept is represented by its six concept properties.


Previous evaluations using this corpus (Dunn, 2013b) concluded that prepositions annotated as metaphoric in the corpus should not be considered metaphoric for computational purposes.

metaphorically used prepositions have been untagged as metaphoric.


old for metaphor was set independently for each genre and tied to the number of sentences containing metaphorically used words, as rated by the annotators of the corpus.

for the number x of metaphors in the genre, the x sentences with the top metaphoricity values were identified as metaphoric.


Further, it can be applied to any English text without the need for labelled training data.

the scalar approach performs competitively on a binary task (0.608 vs. 0.638 F-Measure) but can also produce scalar identifications, which binary systems cannot produce.


For the monolingual bigram model, the number of states in the HMM is U times more than that of the monolingual unigram model, as the states at specific position of F are not only related to the length of the current word, but also related to the length of the word before it.

its complexity is U 2 times the unigram model's complexity: The bilingual expectation is given by Eq.


Moreover, the proposed method yields 0.96 BLEU improvement relative to supervised word segmenters on an out-of-domain corpus.

we believe that the proposed method would benefit SMT related to low-resource languages where annotated data are scare, and would also find application in domains that differ too greatly from the domains on which supervised word segmenters were trained.


Then, just like in the standard beam search approach, we prune all explored new hypotheses and continue with the pruned set of B many hypotheses.

for a cipher of length N we only explore N • B • (B LM + B lex ) many hypotheses.


Using a set of gold derivations, we can train the network so that correct order will receive a high score by Equation 4and incorrect one will receive a low score.

when we merge the candidates of two adjacent spans during BTG-based decoding, the local score in Equation 4is useful in two aspects: (1) for the same merging order, it evaluates how well the two candidates are merged; (2) for the different order, it compares the candidates generated by monotone order and inverted order.


For another, DSN focuses on the entire derivation structure, rather than only the two candidates for merging.

the translation decoder will pursue good derivation structures via DSN.


However, it is too difficult to obtain all derivations.

we use n-best derivations of SMT decoding to simulate the complete derivation space, and take them as the derivations in A(u i ).


We restrict the types to the most important types -following the 11 chunk types annotated in the CoNLL-2000 chunking task (Sang and Buchholz, 2000) -by replacing all other types with a new type OUT.

"Analysts" is labeled B G ; "much", B NP ; "will", B VP and so on.


Problem (2), however, depends on the syntactic structure of the sentence prefix.

it is not possible to determine the VNs before parsing commences, but the decision has to be made while parsing is going on.


For words marked as predictable their existence and word class, but not their lexicalization and position can be predicted.

we replace the lexical item with "[virtual]" and generalize the part-of-speech tag to a more coarse grained one.


we adopt the enhanced mode of evaluation proposed by : In addition to the accuracy for whole sentences, the accuracies of the n newest words of each analysis are computed.


For example, we can obtain "Russia deployed an aircraft carrier", but not "Russia deployed at (在...部署) an aircraft carrier".

we cannot acquire any common entity pair like (Russia, aircraft carrier) for deploy and 在...部署 (deploy at).


On the other hand, it can be correctly translated using EF.

we produce the hybrid relation translation, and we empirically set λ = 0.4: In this section, we evaluate the proposed approach on the entity translation task and the relation translation task.


We normalize the word scores by dividing them by the score of the 200th word.

only the top 200 words have scores greater than or equal to 1.0.


In TTL, however, training and testing samples cannot be assumed to have the same distributions.

noise detection methods used in semisupervised learning are not directly suited in TTL.


In any iteration during TTL, the hypothesis space is the same and the probability δ is fixed.

the least error boundary is determined by the size of the transferred sample m and the class noise of transferred samples η.


(2014) report 52.8 BLEU for Arabic-English and 34.7 BLEU for Chinese-English.

our results are around 1.3-1.4 BLEU point better.


Freezing weights learned in unsupervised training ( Figure 2) performs better than fine-tuning them; also, Table 3 shows that the best result is achieved if no convolution is used in mfCNN.

the best configuration for paraphrase identification is to "forward" fixed-size interaction matrices as input to the logistic regression, without any intermediate convolution layers.


Rule 5 is similar to rule 4, capturing the taxonomic relationship between target variables COARSE-ASPECT and FINE-ASPECT.

by using conjunctions to combine features and appropriately weighting these rules, we account for the uncertainties in the underlying features and make them more robust.


In contrast, PSL-Joint uses both coarse and fine SeededLDA scores and captures the dependency between a coarse aspect and its corresponding fine aspect.

pSL-Joint is able to distinguish between LECTURE-CONTENT and QUIZ-CONTENT.


And, for all fine aspects, the correct label is found in the top 3 predictions around 40% of the time.

using the top three predictions made by PSL-Joint, we can understand the fine aspect of the post to a great extent.


The resource is a merger of WordNet with multiple other lexical resources, the most prominent of which is Wikipedia.

the manually-curated information in WordNet is augmented with the complementary knowledge from collaborativelyconstructed resources, providing a high coverage of domain-specific terms and named entities and a rich set of relations.


Contrasting word pairs have the broadest meaning among them, referring to word pairs having "some non-zero degree of binary incompatibility and/or have some non-zero difference across a dimension of meaning."

by definition, opposites are a subset of contrasting word pairs (refer to (Mohammad et al., 2013) for detailed discussions).


Disagreement, rather than agreement, between authors prompts many responses.

reply links are more likely disagreements when measured at the post level, as seen in Ta- Table 3: Post stance classification accuracy and standard deviations for 4FORUMS (left) and CREAT-EDEBATE (right), estimated via 5 repeats of 5-fold cross-validation.


It can be seen as the intrinsic dimensionality of a latent embedding of the elementary feature vectors.

by imposing a low-rank penalty on the tensor parameters we are encouraging the model to induce a lowdimensional projection of the elementary feature vectors .


Although event extraction depends on name identification and entity mention co-reference, it is another difficult task in ACE evaluation and not the focus in the event extraction task.

in this paper, we directly leverage the entity label provided by the ACE, following most previous works (Hong et al., 2011;Liao and Grishman, 2010;Ji and Grishman, 2008).


To make an accurate prediction, it is necessary to capture the most valuable information with regard to the change of the candidate words.

we split each feature map into three parts according to the candidate argument and predicted trigger in the argument classification stage.


Some SFV systems have used this provenance information to help filter and combine extractions (Sammons et al., 2014).

we also explored enhancing our stacking approach by including additional input features that capture provenance information.


This scheme is not applicable to slot types whose fillers are not entities (like date or age).

simpler matching schemes are used to eliminate redundancies for these slot types.


We note that they run the same exact system on 2013 and 2014 data.

in order to have a better understanding of our results, we plot a learning curve by training on different sizes of the 2013 SFV data and using the scorer to measure the F1 score on the 2014 SFV data for the 10 common systems.


Although there are certain parts of the dataset when the F1 score drops which we suspect is due to overfitting the 2013 data, there is still a strong correlation between the 2013 training data size and F1 score on the 2014 dataset.

we can infer that training on 2013 data is useful even though the 2013 and 2014 data are fairly different.


However, deciding which part of trees to use as L is not trivial, and applying this approach requires that we predict L and R using different methods.

in this study, we use the same method to predict both sequences of constituents for simplicity.


From the figure we can see that lengths of up to 2 constituents cover the majority of cases for both L and R, but a significant number of cases require longer strings.

methods that predict a fixed number of constituents are not appropriate here.


In contrast, NN and IN will probably be inserted most cases.

the appearance of such ambiguous constituents in the training data is less consistent than that of necessary syntactic constituents, and thus the prediction model avoids generating such ambiguous constituents.


For example, the word reordering in Figure 1(a) can be represented with the BTG parse tree in Figure 1(b).

2 the task to reorder an input source sentence can be solved as a BTG parsing task to find an appropriate BTG tree.


If Condition (3) does not hold, there are i and j which satisfy π i < π j ∧ y i > y j ∧ y i ̸ = −1 ∧ y j ̸ = −1, and π i and π j are not compatible with y.

s ′ is valid only if Condition (3) holds.


y does not contain the 2413 nor the 3142 patterns, and any subsequence of y also does not contain the patterns.

[p, r) and [r, q) are separable permutations.


The hash values are used as feature names.

when accessing feature weights stored in a hash table using the feature names as keys, the keys can be used as their hash values.


It is very important to note that we use a parallel corpus to train the model.

we directly identify target-side topical items for source topical words via word alignments rather than sampling.


(2013) proposed a simple negative sampling method, which generates r noise samples for each input word to estimate the target word, in which r is a very small number compared with N .

the training time yields linear scale to the number of noise samples and it becomes independent of the vocabulary size.


During the procedure of learning word representations from the context words in the sliding window, if the central word w k hits the category information, the corresponding optimization process of the metadata powered regularization function will be activated.

we maximize the weighted Euclidean distance between the representation of the central word and that of its similar words according to the objective function in Equation (5).


If a candidate question is considered semantically similar to the query, the student will label it as "relevant"; otherwise, the student will label it as "irrelevant".

each candidate question gets three labels and the majority of the label is taken as the final decision for a querycandidate pair.


The question understanding results of paraphrases should be same.

the representations of two paraphrases computed by the same column of MCCNNs should be similar.


Adjective-noun composition is modeled as the functional application of an adjective meaning (represented as a matrix) to a noun meaning (represented as a vector).

the combination of an adjective and noun is the product of the matrix ADJ and the vector noun as shown in Figure 1.


Context representation in conventional models are limited: First, phrases cannot be used as features directly because of the data sparseness problem.

phrases are backed off to low-order representation such as bigrams and trigrams.


For each feature group X g , we learn a V g × D g embedding matrix E g that applies the conversion: where we apply the computation separately for each group g and concatenate the results.

the embedding layer has E = g F g D g outputs, which we reshape to a vector h 0 .


These parameters will be trained using the perceptron.

in decoding with the perceptron-trained model, we will use beam search to attempt to find: each decision y j receives a score: in the perceptron with early updates, the parameters v(y) are trained as follows.


Since the learning problem is nonconvex, different initializations of the parameters yield different solutions to the learning problem.

for any given experiment, we ran multiple random restarts for every setting of our hyperparameters and picked the model that performed best using the held-out tune set.


In addition to adding elements to the end of the sequence, the stack LSTM provides a pop operation which moves the stack pointer to the previous element (i.e., the previous element that was extended, not necessarily the right-most element).

the LSTM can be understood as a stack implemented so that contents are never overwritten, that is, push always adds a new entry at the end of the list that contains a back-pointer to the previous top, and pop only updates the stack pointer.


Beam search was determined to have minimal impact on scores (absolute improvements of ≤ 0.3% were possible with small beams).

all results we report used greedy decoding- Chen and Manning (2014) likewise only report results with greedy decoding.


Although many of these unmatched relations are indeed incorrect, the dataset is sufficiently biased towards the STOP action that the occasional false negative hurts end-to-end performance.


Even a syntactically correctly attached PP can still be semantically ambiguous with respect to questions of machine reading such as where, when, and why.

when extracting information from prepositions, the problem of preposition sense disambiguation (semantics) has to be addressed in addition to prepositional phrase attachment disambiguation (syntax).


In our experiments we used both kinds of models, but found the discriminative model performed better.

we present details only for our discriminative model.


Recall that a PP 5-tuple of the form {n0, v, n1, p, n2}, whose enclosed PP attaches to the verb v, denotes a ternary relation with arguments n0, n1, & n2.

we can extract a ternary relation from every 5-tuple for which our method predicts a verb attachment.


We find that while predicted labels are of higher precision than directly projected labels, they are of lower precision than labels post filtered projection.

for filtered projection, it makes more sense to allow predicted labels to only supplement projected labels.


Since the precision of labels generated by the SRL is lower than the precision of labels obtained from filtered projection, the precision of the training data is expected to decrease with the increase in recall.

starting with a high k seeks to ensure high precision labels are added to the training data in the first iterations.


1), it provides no such guarantee on precision.

it is important to experimentally decide an early termination cutoff before the SRL gets overtrained.


If a sentiment word is not modified by negation words, the value of its negation features is set to 0.

the sentiment words and their corresponding negation features in English and Chinese are adopted to represent the document pairs (x E , x C ).


Parameter l } is learned by maximizing the objective function according to the sentiment polarity label s i of document d i : Through the supervised learning phase, [W E , W C ] is optimized by maximizing sentiment polarity probability.

rich sentiment information is encoded into the bilingual embeddings.


The intuition behind this is that the introductory sections of papers that cite these target document summarize the research in papers from the target document set as well as the relationships between these papers.

these introductions can be thought of as mini-surveys for specific aspects of the topic; combining text from these introductory sections should allow us to generate good comprehensive survey articles for the topic 1 .